CAREER: Data to Operational Decisions: A Predictive Analytics Approach

这项教师早期职业发展（Career）计划将开发数据驱动的建模和学习技术，以提高运营决策的准确性。重点将放在易于实施的技术上，这些技术在企业和社会面临的广泛决策中“开箱即用”：设计正确的产品，向客户提供正确的产品和价格，每种产品的正确数量，甚至是为个别行业或人口群体提供正确的政策组合（税收，补贴等）。解决该问题的现有方法是选择合适的模型，从数据中学习模型参数，然后在模型下解决决策问题。重点主要是学习模型（通常，独立于特定的决策上下文）或有效地解决给定模型的决策问题，将模型选择留给专家。相比之下，这项研究将采取端到端方法：从一种数据类型（购买交易、点击流、营销研究、保险政策选择等）开始，以运营决策结束。集成方法将通过自动选择针对数据和决策定制的模型来“开箱即用”地工作。通过限制对专家投入的需求，这项研究将大大增加数据驱动决策方法在广泛行业的应用，极大地有利于美国经济和社会。对于端到端方法，研究将开发不仅具有高预测能力，而且易于有效优化（解决决策问题）的建模技术。然而，挑战在于复杂的预测模型缺乏有效优化的结构，而可以有效优化的简约模型缺乏预测能力。为了平衡这种紧张关系，研究将重点放在分布的模型类别上，而不是偏好（排名）列表。这个模型类非常普遍（植根于古典经济效用理论），并捕获了个人对产品或政策提供的广泛反应。模型类除了具有通用性外，还具有丰富的及物性结构。该研究将开发算法解决方案，使用数据识别一般模型类的实例，然后利用传递性结构进行有效优化。此外，该研究将开发新的分析技术来量化算法的性能。性能分析具有挑战性，因为它需要量化“模型复杂性”和“数据复杂性”。该研究将利用传统的工程技术，如压缩感知和布尔函数分析，开发新的“复杂性”度量。

This Faculty Early Career Development (CAREER) Program grant will develop data-driven modeling and learning techniques to improve the accuracy of decision making in operations. The focus will be on easy to implement techniques that work "out-of-the-box" for a wide-range of decisions faced by businesses and society: the right products to design, the right products and prices to offer to customers, the right quantity of each product to carry, and even the right policy mix (taxes, subsidies, etc.) to offer to individual industries or demographic groups. The existing approach to this problem is to select an appropriate model, learn model parameters from data, and then solve the decision problem under the model. The focus is mainly on either learning the model (typically, independent of the specific decision context) or efficiently solving the decision problem given the model, leaving model selection to an expert. In contrast, this research will take an end-to-end approach: starting with a type of data (purchase transactions, click-streams, marketing studies, choice of insurance policies, etc.) and ending with an operational decision. The integrated approach will work "out-of-the-box" by automatically selecting a model customized to the data and the decision. By limiting the need for expert input, the research will significantly increase access to data-driven decision methodologies to a wide-range of industries - greatly benefiting the U.S. economy and society. For the end-to-end approach, the research will develop modeling techniques that not only have high predictive power, but are amenable to efficient optimization (solving of the decision problem). The challenge however is that complex predictive models lack structure for efficient optimization, whereas parsimonious models that can be efficiently optimized lack predictive power. In order to balance this tension, the research will focus on the model class of distributions over preference (ranked) lists. This model class is very general (is rooted in the classical economic utility theory) and captures a wide range of responses of individuals to product or policy offerings. Despite its generality, the model class also possesses rich transitivity structure. The research will develop algorithmic solutions that use data to identify an instance of the general model class and then exploit the transitivity structure to efficiently optimize. Further, the research will develop novel analysis techniques to quantify the performance of the algorithms. Performance analysis is challenging because it requires quantifying "model complexity" and "data complexity". The research will develop novel "complexity" metrics using traditional engineering techniques such as compressive sensing and Boolean function analysis.